Sequencing of the mink genome: plans and perspectives
Bibliographic record
Abstract
The assembled sequence of the whole genome is a must nowadays in any industrially important species. The aim of this research program is to provide the industry and the community with a robust mink genome sequence assembly. It shall be complemented with the identification of widely distributed polymorphic single nucleotide polymorphisms (SNPs) organized into an array designed for undertaking linkage and association studies necessary for mapping, identifying and characterizing genes underlying any specific traits of interest in American mink. Genome assembly based on the sequences of individual clones from the mink Bacterial Artificial Chromosome (BAC) library would result in a draft genome of high quality and accuracy. We explore an alternative approach, called BAC-NGS (from next generation sequencing) that utilizes the BAC library as a solid backbone, complemented with shotgun sequences of the whole genome, for filling gaps for the final assembly. We tested different settings of the new approach in several pilot projects in order to assign the most efficacious approach to sequence assembly. Approximately 15% of the mink genome has so far been generated and assembled by means of this method. This sequencing project represents a long-term investment in the future mink fur industry and farming management, enhancing the development of applications in academic and industrial research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".